5,766 research outputs found
Single-Scan Min-Sum Algorithms for Fast Decoding of LDPC Codes
Many implementations for decoding LDPC codes are based on the
(normalized/offset) min-sum algorithm due to its satisfactory performance and
simplicity in operations. Usually, each iteration of the min-sum algorithm
contains two scans, the horizontal scan and the vertical scan. This paper
presents a single-scan version of the min-sum algorithm to speed up the
decoding process. It can also reduce memory usage or wiring because it only
needs the addressing from check nodes to variable nodes while the original
min-sum algorithm requires that addressing plus the addressing from variable
nodes to check nodes. To cut down memory usage or wiring further, another
version of the single-scan min-sum algorithm is presented where the messages of
the algorithm are represented by single bit values instead of using fixed point
ones. The software implementation has shown that the single-scan min-sum
algorithm is more than twice as fast as the original min-sum algorithm.Comment: Accepted by IEEE Information Theory Workshop, Chengdu, China, 200
Noisy Gradient Descent Bit-Flip Decoding for LDPC Codes
A modified Gradient Descent Bit Flipping (GDBF) algorithm is proposed for
decoding Low Density Parity Check (LDPC) codes on the binary-input additive
white Gaussian noise channel. The new algorithm, called Noisy GDBF (NGDBF),
introduces a random perturbation into each symbol metric at each iteration. The
noise perturbation allows the algorithm to escape from undesirable local
maxima, resulting in improved performance. A combination of heuristic
improvements to the algorithm are proposed and evaluated. When the proposed
heuristics are applied, NGDBF performs better than any previously reported GDBF
variant, and comes within 0.5 dB of the belief propagation algorithm for
several tested codes. Unlike other previous GDBF algorithms that provide an
escape from local maxima, the proposed algorithm uses only local, fully
parallelizable operations and does not require computing a global objective
function or a sort over symbol metrics, making it highly efficient in
comparison. The proposed NGDBF algorithm requires channel state information
which must be obtained from a signal to noise ratio (SNR) estimator.
Architectural details are presented for implementing the NGDBF algorithm.
Complexity analysis and optimizations are also discussed.Comment: 16 pages, 22 figures, 2 table
Fast Min-Sum Algorithms for Decoding of LDPC over GF(q)
In this paper, we present a fast min-sum algorithm for decoding LDPC codes
over GF(q). Our algorithm is different from the one presented by David Declercq
and Marc Fossorier in ISIT 05 only at the way of speeding up the horizontal
scan in the min-sum algorithm. The Declercq and Fossorier's algorithm speeds up
the computation by reducing the number of configurations, while our algorithm
uses the dynamic programming instead. Compared with the configuration reduction
algorithm, the dynamic programming one is simpler at the design stage because
it has less parameters to tune. Furthermore, it does not have the performance
degradation problem caused by the configuration reduction because it searches
the whole configuration space efficiently through dynamic programming. Both
algorithms have the same level of complexity and use simple operations which
are suitable for hardware implementations.Comment: Accepted by IEEE Information Theory Workshop, Chengdu, China, 200
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